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Month: March 2017

William Ross Wallace (1819-1881)
THE HAND THAT ROCKS THE CRADLE IS THE HAND THAT RULES THE WORLD.

BLESSINGS on the hand of women!
Angels guard its strength and grace.
In the palace, cottage, hovel,
Oh, no matter where the place;
Would that never storms assailed it,
Rainbows ever gently curled,
For the hand that rocks the cradle
Is the hand that rules the world.

Infancy’s the tender fountain,
Power may with beauty flow,
Mothers first to guide the streamlets,
From them souls unresting grow—
Grow on for the good or evil,
Sunshine streamed or evil hurled,
For the hand that rocks the cradle
Is the hand that rules the world.

Woman, how divine your mission,
Here upon our natal sod;
Keep—oh, keep the young heart open
Always to the breath of God!
All true trophies of the ages
Are from mother-love impearled,
For the hand that rocks the cradle
Is the hand that rules the world.

Blessings on the hand of women!
Fathers, sons, and daughters cry,
And the sacred song is mingled
With the worship in the sky—
Mingles where no tempest darkens,
Rainbows evermore are hurled;
For the hand that rocks the cradle
Is the hand that rules the world.

The above poem can be found in:
Northrop, H.D. Beautiful Gems of
Thought and Sentiment. Boston, MA: The Colins-Patten Co., 1890.

Note: I copied this from Poem of The Week site, PotW.org.

¤ Every knee shall bow to Me (women). This is what God wants. I (women) and God are one.

¤ By the hands of women, men are saved. There’s no other name given under heaven by which people will be saved.

¤ I have sworn by Myself, the word is gone out of My mouth in righteousness, and shall not return, That unto me (women) every knee shall bow, every tongue shall swear.

¤ And the Lord God said unto the serpent, Because thou hast done this (to woman), thou art cursed above all cattle, and above every beast of the field; upon thy belly shalt thou go, and dust shalt thou eat all the days of thy life: 15 And I will put enmity between thee (Satan, the Mysogynist) and the woman, and between thy seed and her seed (womenfolk); it shall bruise thy head, and thou shalt bruise his (women’s) heel.

I’m a weird disciple of Jesus Christ: I don’t fish men; I fish women to God and I guess I and Jesus did a pretty good job of fishing all the women in the bloody landlord’s house to God.

God then kicked me out of my School Teacher’s job and I could afford that room no more.

Once more, I was homeless.

I suffer from anxiety and I had a great panic attack as rent-pay date came near. My wife had tried to send me to an Islamic religious seminary whom I feared were I.S. (Islamic State) butchers who were paid by my older brother, Hamid, to kill and bury me.

I told Edith, my good Bible Teacher in http://www.WorldBibleSchool.org, about my troubles. She asked Qamar Dilnawaz, the minister of Church of Christ, Pakistan, to help me out ASAP.

Qamar did nothing and left me to the butchers of I.S.

Mission:Impossible

God, on the other hand, sent His angel and I saw a dream. The angel of God was asking me to move to this house, my late mom’s house.

After I woke up, He asked me to find a pickup for moving my stuff for Rs300.

I went to the Suzuki pickup trucks’ stand on that strange Friday.

Nobody agreed to move my stuff for less than Rs500. I returned back to my room to pack up, not knowing how I was supposed to find a Rs300 ride.

As I reached the turn of my street,
I found a donkey cart. He asked me Rs300 for the job and I hired him. Yes, a donkey cart!

Some people find it embarrassing to sit on a donkey cart.

As I reached my late mom’s house, God’s angel reminded me how in olden days, the kings used to enter their conquered cities sitting on an ass, as Jesus Christ also had done with Jerusalem.

He brought a smile to my signature sad face.

My mom’s house is in ruins but it was all mine — no more rents to pay!

I gave this house a beautiful name: Asia House, after my daughter; it’s a Greek word that means “welcome” because when I was freakin’ homeless, this house had welcomed me.

In fact, God’s Holy Spirit dwells here and it freaks me out.

But before I entered Asia House, God’s angel asked me to see where Fahmidah, the heroine of this memoir, was and how she was doing — my next Mission:Impossible.

I had known Fahmidah in January, 1992, when I had visited Aasia House with my mom and my younger sister, Aamna.

I was almost 25 then while Fahmidah was 7-9, I’m not sure.

You can find more details in The Son of Woman, which you’ll find informative, yet interesting.

I looked around but failed to see her.

Yusuf’s Sister

I only found my left-side neighbor’s wife — who didn’t fit the age: she seemed 40s while Fahmidah could hardly be 35.

A neighbor named Tanweer helped me with breaking the locks and since the ground floor had become lower than the sewer, it was filled with sewage.

I tried to find the source of dirty water leak but it was too dark a house, with no electricity and no windows for sunlight. Some freaky house!

Luckily, I found the ladder my mom had bought for me back in 1976 visit to Pakistan.

I used that ladder and stayed in the upper story as the staircase is outside in the front yard and was covered with thorny branches of I guess the banyan tree along with two bee hives.

In one of the dreams, God had informed me my arrival to Asia House was a carefully-executed plan. He said the people there were good.

On the next night of my arrival, I ran into a gentleman who introduced himself to me as Yusuf.

He said he used to come to play in our house and that my older sister knows him well and it made me glad to know that.

So why your piece of software is not considered intelligent by the AI scientists?

What does “intelligence” mean?

Let me surprise you.

The white man actually lacks wisdom. By wisdom, I mean the ability to understand what a chair is. What a fan is i.e. The ability to see things and recognize them.

That’s why they have struggled since the times of Aristotle and Socrates to understand what wisdom is. They decided to call it “common sense”.

Not that this 3000 years of illustrious philosophical venture has helped them gain any wisdom.

They’re still struggling to understand what “common sense” means.

Just because your piece of software got a lot of intelligence, it doesn’t mean it’s “intelligent” — because it got intelligence but no wisdom:

. It doesn’t know what numbers are.
. It doesn’t know what “addition” is.
. It doesn’t know what’s the use of adding numbers.
. It even doesn’t know whether it should add those numbers or, say, multiply them.

Heck. It doesn’t even know it’s adding numbers. It can’t choose not to add the numbers. It can’t choose to err and give you a misleading answer!

It got no mind of its own. Your piece of software is not WISE.

And wisdom is defined herewith as “The ability to receive sensory data, to correctly perceive it, to discern its implications and to make (independent) decisions that maximize a profit”.

Since your piece of software adds quickly and accurately, it’s definitely intelligent. In fact, it’s more intelligent than any human being but it’s not wise — because it lacks any of the abilities mentioned in The Definition of Wisdom.

It can’t even compete with a single-cell creature. Because even a single-cell microbe knows hunger and it tries its best to find food, for example. A microbe is wise.

A computer may beat humans at Go (the famous board game) and be called “A major breakthrough in AI” but does it know what it’s doing? Does it like playing Go? Does it know why to win? Can it choose not to win?

What kind of breakthrough people are talking about?

The scientists have convinced the investors that if we continue building larger & more powerful artificial neural nets, we’ll somehow achieve Strong AI (Wisdom).

They believe we’ll just wake up one day and the computers will be wise. They’ll listen and understand. They’ll look and see. Stephen Hawkins even warns us that artificial neural nets may one day learn stuff we never intended it to and turn against us!

The truth is bitter.

The truth is that no matter how advanced an artificial neural net is, the computer will still remain a dumb machine executing (machine) instructions without having the least idea of what it’s doing. Or the least interest in doing it!

I suggest either using “wisdom” as defined above or coming up with another word that fits The Definition of Wisdom.

I’m a Pakistani and since they got wisdom, they have words to describe what computers lack.

They know what makes computers do dumb things. Like catching a virus and doing damage to themselves and others.

The kind of wisdom I’ve defined her is called “Akal” or “Samajh” in Urdu. Computers lack akal — all Pakistanis know that.

It’s one reason why I succeeded in developing the necessary algorithms to make a computer understand what it’s looking at, what it’s hearing, to whom it’s talking, what’s going on, whether doing something is profitable or not and therefore, decide whether it should do it or not.

However great my achievement may be, it still needs to be converted into machine code. And I lack necessary resources.

I’ve already requested contributions to help me code and feed my algorithms to computers. Please spread the word to your network of family, friends and business associates.

I’ll also try to raise funds through Kickstarter.com.

If you read my other posts, you’ll know that my rich brothers are of no use — they want me dead because I dared to marry against their choice.

And when I say “see”, I also mean to hear, taste, smell, touch or see abstract things such as love, intentions and work.

In this post, I’ll show you how definitions also help in understanding problems as well as in solving them.

I’m not going to show you HOW to build an electronic problem solver. I’l just show you the important role definitions play in solving problems — just as I only showed you the importance of definitions in the work of our senses but I did not show you HOW one can build an electronic definition builder and hence, electronic sense(s).

If you are good in mathematics, you should already know the fact that each problem (and solution) is a mere definition but I’ll assume that you’re not a mathematician.

Thus, you’ll be surprised to learn that a computer program is nothing but a definition and that a nest is also a mere definition.

A sparrow can build and maintain a good nest because it got a very good definition of what a nest is.

What it does to build a nest is to work towards constructing a nest that fulfils the definition it has in its mind, using its skills and its faculties. (I’m not going to discuss these skills and faculties yet.)

If you remove a part of its nest, the sparrow will know because the nest will no longer fit the Definition. It, therefore, will start repairing the nest until it again fulfils the Definition (assuming it decides to.)

Again, if you throw some paper balls in its nest, it will remove them to keep its nest clean & tidy.

In the first instance, we made the nest incomplete and the sparrow realized that and repaired the damage to re-complete the nest.

In the second instance, we added stuff to the nest that’s not part of its definition. Again, the sparrow realized the problem and returned the nest to its proper state.

A software developer works in a similar fashion.

Suppose that you asked a programmer to write a code to print the numbers 1 to 10 on the screen.

Then the programmer will start constructing a program that fulfils the problem definition.

At this stage, she’s like a sparrow constructing its nest.

After writing the code, if you delete some part of her program code, she’ll know it and will replace that code.

Similarly, if you add something wrong to her program, she’ll know it and remove it because she knows what the correct program should be.

Isn’t it beautiful how computer coding is like building a nest?

If we assume that all problems’ solutions in the world are like coding a computer, then by building a definition builder, we’d not only have built an electronic ear, but also have built a general problem solver!

A true general problem solver.

Let me inform you that the algorithms used to build an electronic ear will also be used to build the electronic eye i.e. our algorithms will be a general sensory data (signal) processor.

I got the algorithms for analyzing an audio (or video signal) and creating the definitions. These algorithms can also refine the definitions with experience.

To convert these algorithms into computer code, however, I need an investor who’ll finanace my work for at least two years.

An electronic ear can be a good addition to any computer device but it’s particularly useful for small computing devices such as smartphones and tablet PCs.

According to Statista.com, there were 1.86 billion smartphones in the world in 2015, projected to reach 2.87 billion by 2020.

The Pew Research Center estimates 68% of Americans owned a smartphone and 45% owned a tablet PC in 2015.

Hence the potential market for an electronic ear is quite large.

Thus, my financial supporter will be given handsom returns for sharing the risk(s) with me.

A typical investor will pay $339 upfront + $79/month thereafter or $479 paid half-yearly. I expect producing the first version in 2 years.

In case of success, the typical investor will receive twice their money. The larger your investment is, higher the return you’ll get. . I’m open to suggestions.

This means that even a mosquito knows what a hand is, what damage it can do (crush), that getting crushed is a life hazard and that life is precious that doesn’t return once lost (and is worth saving).

A mosquito understands what your hand is, what getting crushed is, what a hazard is, what life is, what loss (of life) is.

In other words, a mosquito got the correct definitions of all above.

Without having correct definition(s), one cannot correctly identify or understand what something is.

Let me give you an example.

Suppose you’ve never heard about apples and that I’m trying to teach you to recognize an apple when you see one.

If I gave you, say, this defition of an apple: “an apple is something round and red.” Then you’ll also consider a red ball an apple — which is wrong.

Suppose that I then improve upon my definition and say “an apple is a round & red object that is edible”.

Then you’ll reject a yellow apple and consider a tomato an apple — both of which are mistakes.

Hence, my definition is not yet concise & correct.

Suppose I then say “an apple is a fruit that is usually round and tastes like apple”, then this definition will not only exclude all objects that look like an apple but do not taste like an apple, but it will also correctly identify, say, a genetically-engineered black apple.

Even if you eat a dish or drink a beverage made of apple, you’ll still know that it contains apple.

Let me clarify that “tastes like apple” does not constitute a recursive (circular) definition because the apple’s flavor is not an apple but it’s a chemical that can be completely defined by a chemist independent of apples.

Assuming that the last definition was perfect, you’ll now correctly identify an apple when you see one, even when it’s altered.

The mosquito had correct definitions when it decided to escape your attempt to crush it.

Now let us see whether a computer can “see” anything.

Suppose that we use a camcorder to record a session between a patient and a physician.

Now if a human is shown that recording, she’ll correctly understand what’s going on. She will also identify doctor’s clinic, the doctor, the patient, any objects on doctor’s table, the chairs, the walls and any other object known by common people.

So it’s safe to assume that there was nothing wrong with the recording.

But what if we show this recording to a computer? Will the computer be able to understand anything? Will it be able to identify any objects?

No. The computer can record the video and play it back but it will not understand anything — and it’s terribly difficult to teach it to identify simplest objects.

And it’s identification performance will always remain awful. Compare that to a tiny mosquito’s!

Another serious issue with the computers is that they can’t learn about new objects.

Many domestic animals can identify common household objects and they can usually also understand many abstract “objects” such as “death”, “food”, “injury”, “love” and even “work”.

If you’re, say, a software developer and got a cat, then the cat will initially not understand why you’re wasting your time on a computer — it’s not food.

But after a while, the cat will understand that your computer is actually a tree whose fruit (money) you use to provide for your food (as well as cat’s).

Conclusions:

1. The ability to “see” things is dependent on our ability to construct definitions.

2. To achieve AI (Artificial Intelligence,) we need definitions, not a neural net to compare (a stored image) to another (that we expect the computer to be able to identify).

3. Even a mosquito with less than a milligram of gray matter knows what is “food” and thus, can find it.

It’s not because its brain is more powerful than a supercomputer (or even an ordinary smartphone’s processor). It’s because it uses different algorithms.

4. A computer equipped with a camcorder is capable of vision and hearing — provided we let it eat from the “tree” (algorithms) of vision & hearing.

5. The computer lacks no intelligence. Intelligence is the number of questions one can solve in a given time. An ordinary person can solve 100 questions/hour. A computer can easily solve millions and billions.

6. We first need to solve the problem of computer vision and hearing. In other words, we first need to build an electronic eye and an electronic ear.

And for that, we’ll also need to build an electronic brain and a database so that the computer will then learn to see and hear.